AI makes development faster while increasing competition. What changes when internet projects become easier to build, distribute and copy?
AI dramatically lowers the cost of a first version
AI can now help with almost every early stage of a project: structuring an idea, researching a market, preparing interview questions, drafting copy, generating interface options, writing and debugging code, creating database structures, testing, documentation, translation and support. For a small experiment, a founder can often build a landing page or prototype without hiring a development company.
That does not mean serious software has become a few prompts. Long-lived systems still need architecture, security, integrations, data quality, testing, monitoring and maintenance. AI reduces the cost of building; it does not remove engineering judgement.
When everyone can build, competition increases
Ideas that once stopped at a €50,000 development estimate can now reach the market in days or weeks. The same is true for competitors. Technical execution becomes cheaper, so choosing the right problem, understanding a narrow audience and reaching that audience become more valuable.
The result is a strange combination: it is cheaper to build and often harder to sell. AI can create ads, emails, videos and SEO content, but everyone else can create more content too. Distribution, trust and a clear reason to choose one project over another matter more, not less.
SEO changes, but does not disappear
Search engines increasingly answer questions directly, so ranking does not guarantee a click. The basic work still matters: a technically accessible site, clear structure and genuinely useful original content. At the same time, more people will use general AI interfaces for tasks that previously required several separate websites.
A weak AI business is only a wrapper
Using another company’s AI infrastructure is not automatically a problem. The weak model is when almost all value comes from forwarding a user prompt to a public model and displaying the answer in a nicer window. If the user can reproduce 80% of the result with one good ChatGPT prompt, the project is fragile.
A stronger model combines AI with something harder to copy
More defensible projects combine AI with specialised workflows, proprietary or accumulated data, integrations that perform real actions, human expertise, physical delivery, a community, brand trust, regulation, or a distribution advantage. Duolingo is a useful example: generative AI sits inside an existing learning system with curriculum design, progress data, gamification, audience and subscription economics.
A good question is not “Can we add AI?” but “Does AI materially improve the customer’s result or make a previously uneconomic project possible?”
A practical AI risk test
Before building, ask: Could a general AI model add our main function tomorrow? What do we own besides access to the model? Does the project know something a general model does not? Does it take real actions, or only generate answers? Does it improve from our own data? Do we have a distribution channel or trusted relationship?
AI reduces building risk while increasing market risk. The opportunity is enormous, but a durable company still needs something more than generated code and a prompt.